1. Field of the Invention
The present invention generally relates to the geological study of earth formations for the location and exploitation of mineral deposits using electrofacies analysis. More particularly, the present invention relates to a new method for identifying formations of mineral deposits using a user-friendly and reliable ordering technique to order the electrofacies for different sets of log data and interpretation rules.
2. Description of the Related Art
Mineral and hydrocarbon prospecting is based upon the geological study and observation of formations of the earth""s crust. Correlations have long been established between geological phenomena and the formation of mineral and hydrocarbon deposits that are sufficiently dense to make their exploitation economically profitable.
The study of rock and soil facies encountered while prospecting for minerals takes on particular importance. As used herein, a facies is an assemblage of characteristics that distinguish a rock or stratified body from others. A facies results from the physical, chemical and biological conditions involved in the formation of a rock that distinguish it from other rocks or soil. This set of characteristics provides information on the origin of the deposits, their distribution channels and the environment within which they were produced. For example, sedimentary deposits can be classified according to their location (continental, shoreline or marine), according to their origin (fluviatile, lacustrine, eolian) and according to the environment within which they occurred (estuaries, deltas, marshes, etc.). This information in turn makes it possible to detect, for example, zones in which the probability of hydrocarbon accumulation is high.
The set of characteristics used to define a facies depends on the situation. For example, a lithofacies may be defined by the rock""s petrographic and petrophysical characteristics. These are the composition, texture and structure of the rock. Examples of mineral composition are silicate, carbonate, evaporite, and so on. A rock""s texture is determined by its grain size, sorting, morphology, degree of compaction, and degree of cementation. The rock structure includes the thickness of beds, their alternation, presence of stones, lenses, fractures, degree of parallelism of laminations, etc. All of these parameters are related to the macroscopic appearance of the rock.
For extraction of hydrocarbons from geologic formations, the particularly pertinent characteristics of the lithofacies are the porosity of the reservoir rocks and their permeability, as well as the fraction of the pore volume occupied by these hydrocarbons. These aid in estimating the nature, quantity, and producibility of the hydrocarbons contained in such strata.
There are various sources of information on formation lithofacies. Information may be gathered from subsurface observations such as, for example, by the study of core samples taken from rock formations during the drilling of a borehole for an oil well. Such information can also be provided by drill cuttings sent up to the surface from the bottom of a well by means of a fluid (generally drilling mud) injected near the drilling tool. It is not normally cost-effective to identify facies using these methods. Information on geological formations traversed by a borehole is more commonly gathered by a measurement sonde passing through the borehole. The gathered information as a function of the sonde""s position along the borehole is then stored or xe2x80x9cloggedxe2x80x9d.
Many downhole measurement techniques have been used in the past, including passive measurements such as measuring the natural emission of gamma rays; and active measurements such as emitting some form of energy into the formation and measuring the response. Common active measurements include using acoustic waves, electromagnetic waves, electric currents, and nuclear particles. The sonde measurements are designed to reflect the distinguishing characteristics of the rock facies. Multiple logs and sondes may be used to gather the measurements, which are then correlated and standardized to furnish measurements at discrete levels separated by equal depth intervals. The measurement standardization allows the automation of data interpretation in order to obtain estimates of the rock mineral composition, the porosity of the rocks encountered, the pore size distribution, texture and structure, the pore volume occupied by hydrocarbons, and the ease of flow of hydrocarbons out of the reservoirs in the case of petroleum prospecting. The set of measured formation characteristics values that distinguish the strata in a given borehole is herein termed the electrofacies.
Interpretation studies have demonstrated a strong correlation between the electrofacies and lithofacies, thereby making it possible to identify with confidence the lithofacies of the rocks traversed by boreholes based on the sonde measurements. It has been established that the sets of log measurements (i.e., sample points) which correspond to a given lithofacies form a xe2x80x9cclusterxe2x80x9d in xe2x80x9cdata spacexe2x80x9d. That is, when the measured characteristic values of a formation are graphed, the points generally fall into a continuous region that is distinguishable from the regions where points for other formations would fall.
Electrofacies allow geologists to present log data that describe the cored interval of a petroleum reservoir in a standardized format such as that shown in FIG. 1. In this figure, the leftmost column 110 shows lithofacies of a core description coded using one set of standard interpretation rules (depositional environments are ordered according to a bathymetric profile) and the rightmost column 120 shows the electrofacies obtained by log clustering and after ordering over same depth interval. In terms of the clusters in data space, the electrofacies ordering shown in FIG. 1 may be equated to drawing a path that connects each of the clusters. The sequence in which the path visits the nodes is the order of the nodes. When considered in this manner, the relationship between the clusters is thereby simplified to a single dimension. The electrofacies may then be easily compared to each other, and the vertical distribution of electrofacies in a well, when represented in this standardized format, may more easily be compared to known sedimentary sequences. The ordering of electrofacies also allows a geologist to draw inferences about the geological setting at the time of sediment deposition or to the diagenetic history of the sediments.
Rules to manually order electrofacies are empirical, based on the observation of shale content, cementation, sediment grain size and sorting. These parameters reflect the level of the sediment deposition area with respect to base sea level and reflect the energy of the deposition environment. Thus, the rules used to order electrofacies traditionally allow the geologist to survey the vertical and lateral variation of facies that define the porous sedimentary bodies"" constituent of the petroleum reservoir.
An electrofacies ordering is initially obtained and continuously updated by calibration on core material and log interpretations pertaining to core intervals (log measurements of the formations from which the cores were taken). As discussed below, ordering of the electrofacies requires interpreting the relative positions of electrofacies kernel points in the log space and interpreting the geologic significance of core interval logs. No absolute rules exist for electrofacies ordering, only guidelines adapted for different geological settings that are calibrated in the early stages of the exploration process. Because of the complexity of the calibration process and for economic reasons, core descriptions are often taken as an absolute reference. If log interpretation methods to generate electrofacies do not match the physical core descriptions, then the log interpretation methods are blamed for the discrepancy.
Geologists generally begin electrofacies ordering by projecting log responses onto a set of so-called porosity crossplots (e.g., xe2x80x9cRhoB-Nphixe2x80x9d, xe2x80x9cRhoB-DTxe2x80x9d, xe2x80x9cNphi-DTxe2x80x9d, etc). In order to simplify the ordering process, a third variable (GR, PEF, Resistivity, Neutron-Density Separation etc.) is often displayed as a color-coding of the log responses to both discriminate and identify different soil and sediment formation characteristics.
A typical electrofacies ordering process for a detrital formation made of shales, silts and sandstones, can be summarized as follows. A geologist examines a RhoB vs. Nphi crossplot as shown in FIG. 2a that identifies electrofacies (clusters not specifically shown) of a formation. RhoB is a density measurement, and Nphi is a hydrogen index measurement. High density and high hydrogen index may indicate shaly rock while low density and high hydrogen index may indicate porous clean (non-shaly) sandstone.
The geologist orders the electrofacies in this crossplot based on decreasing shale content and increasing porosity. This means that assignment of electrofacies order number increases the lower right part of the crossplot (shale area) 210 towards its upper right part 220 by following a xe2x80x9cboomerangxe2x80x9d or xe2x80x9cbananaxe2x80x9d shaped path connecting the clusters. The central part 230 of the xe2x80x9cboomerangxe2x80x9d or xe2x80x9cbananaxe2x80x9d is located in the lower left corner as shown in FIG. 2a, on a limestone line with approximately 10% porosity. Higher order numbers are then assigned to highly cemented sections of the formation for electrofacies identified by the lower left part 240 of the neutron-density crossplot as shown in FIG. 2b. Such higher number electrofacies may occur either in deep depositional environments such as upper or lower shorefaces or as a lag at the base of a channel. Because the deposition energy associated with such electrofacies is insignificant compared to the total energy of the depositional environment, these electrofacies are preferably displayed so that they are readily apparent to the geologist (usually by using color coding and indentation in the final ordered electrofacies map).
It is unusual to have only a single crossplot when performing electrofacies ordering. Generally multiple logs are available and manually ordering three log data sets, for example, requires analyzing two or three different crossplots. To order larger numbers of log data sets requires analyzing even more crossplots simultaneously making this process slow and subjective. Ordering of electrofacies manually by the geologist is not an easy task because of the multidimensional nature of the problem. The human brain is very poor in its ability to recognize and manipulate multi-dimensional data distributions and generate an optimal ordering based on the data distribution. Thus, it is desirable to develop a system and method that, in a relatively constant, reliable, and systematic manner, permits automatic ordering of electrofacies to extract information about the geological formation. Despite the apparent advantages of such a system, to date no such system has been implemented.
Accordingly, there is disclosed herein a method for automatically ordering electrofacies to identify formations of mineral deposits. In one embodiment of this method, logs are made over multiple levels within an interval along the borehole in order to obtain a group of several measurements for each of these levels. With each such level of the borehole interval is associated a sample point within a multidimensional space defined by the different logs. The sample point coordinates are a function of the logging values measured at this level. The sample points thus obtained will form a scatter diagram within this multidimensional space.
The sample points of this scatter diagram are used to determine a set of characteristic modes, each corresponding to a zone of maximum density in the distribution of these samples; each mode is regarded as a characteristic of a respective cluster and the surrounding samples of this cluster are related to it. An electrofacies is designated for each of the modes thus characterized, and the electrofacies are ordered in a sequence. A graphic representation is produced as a function of the sequence of electrofacies thus obtained. The characteristic modes of each cluster are made up of sample points coming from the measurements themselves.
Automated ordering of the electrofacies permits geologists to draw inferences about the geological settings in which sediment deposit occurred without directly examining core samples or outcrops. The electrofacies order is determined by (a) training a one-dimensional linear self-organizing map to form an initial neural network that includes a plurality of neurons. The number of neurons is small in comparison to the number of electrofacies kernels (i.e., not greater than one-third the number of electrofacies kernels). (b1) A neuron is selected from the initial neural network. In the next step (b2), the processor determines if more than one electrofacies kernel is attached to the neuron. (b3) If more than one electrofacies kernel is attached to the neuron, then the neuron is split into the number of electrofacies kernels attached to the neuron. (c) Steps (b1)-(b3) are repeated until all neurons in the initial neural network have been processed. In the next step, (d) a self-organizing map is trained to form a final neural network using the split neurons in the initial neural network as initial state. (e) Steps (b1)-(d) are repeated if more than one electrofacies kernel is attached to a neuron with the initial neural network equal to the final neural network resulting from previous iteration. In the last step (f), each electrofacies kernel corresponding to a neuron in the final neural network is correlated to an order number. In the final neural network, more neurons than electrofacies kernels will be present; thus, some neurons may not correspond to an electrofacies kernel.
Training the self-organizing map to form a neural network requires initializing the neurons so that they are interconnected in a one-dimensional array. Electrofacies kernels are input for a given number of cycles to the neural network. In each cycle, each electrofacies kernel is presented to the network, a winner neuron is calculated for each electrofacies kernel that is the nearest neuron to the electrofacies kernel. The winner neuron is moved towards the electrofacies kernel and induces with decreasing intensity its neighbor neurons in the neural network to move towards the electrofacies kernel.
Experimentation confirms that the above method allows accurate ordering of the electrofacies derived from the logging measurements obtained within an interval of geological formations traversed by a sonde traveling in a borehole.